Search Results (1,807)

Modern energy systems are undergoing a profound transformation characterized by the active replacement of conventional fossil-fuel-based power plants with renewable energy sources. This transition aims to reduce the carbon emissions associated with electricity generation while enhancing the economic performance of electric power market players. However, alongside these benefits come several challenges, including reduced overall inertia within energy systems, heightened stochastic variability in grid operation regimes, and stricter demands on the rapid response capabilities and adaptability of emergency controls. This paper presents a novel methodology for selecting effective control laws for low-inertia energy systems, ensuring their dynamic stability during post-emergency operational conditions. The proposed approach integrates advanced techniques, including feature selection via decision tree algorithms, classification using Random Forest models, and result visualization through the Mean Shift clustering method applied to a two-dimensional representation derived from the t-distributed Stochastic Neighbor Embedding technique. A modified version of the IEEE39 benchmark model served as the testbed for numerical experiments, achieving a classification accuracy of 98.3%, accompanied by a control law synthesis delay of just 0.047 milliseconds. In conclusion, this work summarizes the key findings and outlines potential enhancements to refine the presented methodology further. Full article

Weeds are a primary factor affecting sugarcane production and productivity in Thailand. During the late-rainy season, when cultivation is carried out under rainfed conditions, weed competition becomes increasingly severe, prompting farmers to perform secondary weed control using post-emergence herbicides. Therefore, to guide farmers on the appropriate use of herbicides for effective weed management and long-term control during the critical period of sugarcane growth, this study evaluates the effectiveness of pre- and post-emergence herbicides. Conducted in Northeast Thailand using a randomized complete block design (RCBD) with four replications, the experiment revealed that several pre-emergence herbicides, namely pendimethalin + imazapic (825 + 75 g a.i. ha⁻¹), indaziflam (62.5 g a.i. ha⁻¹), and sulfentrazone (875 g a.i. ha⁻¹), and a combination of indaziflam + sulfentrazone (46.88 + 750 g a.i. ha⁻¹) were applied one day after sugarcane planting, demonstrating high weed control efficacy. These treatments significantly reduced the summed dominance ratio (SDR) of both total weed (41.65–78.54%) and dominant weeds (70.13–86.04%), including Digitaria ciliaris (Retz.) Koel., Dactyloctenium aegyptium (L.), Brachiaria distachya (L.) Stapf, and Cyperus rotundus, compared with the no-weeding treatment. In summary, effective weed management in sugarcane fields under late-rainy season can be achieved through the application of pendimethalin + imazapic at 825 + 75 g a.i. ha⁻¹, which produced the highest sugarcane yield (a 139.00% increasing compared with no weeding) and net profit (a 79.75% increasing compared with hand weeding) in loamy sand soil conditions, where D. ciliaris, D. aegyptium, and C. rotundus were dominant weeds. Similarly, indaziflam at 62.5 g a.i. ha⁻¹ yielded the best results (a 71.68% increasing compared with no weeding) and net profit (a 121.04% increasing compared with no weeding) in sandy loam soil, where B. distachya was the only dominant weed. This weed management strategy is potentially transferable to sugarcane production systems in other regions that share comparable soil properties, climatic conditions, and dominant weed species. Full article

Steel products play an important role in daily production and life as a common production material. Currently, the quality of steel products is judged by manual experience. However, various inspection criteria employed by human operators and complex factors and mechanisms in the steelmaking process may lead to inaccuracies. To address these issues, we propose a learning-based method for steel quality prediction, which is named DRBoost,based on multiple machine learning techniques, including Decision tree, Random forest, and the LSBoost algorithm. In our method, the decision tree clearly captures the nonlinear relationships between features and serves as a solid baseline for making preliminary predictions. Random forest enhances the model’s robustness and avoids overfitting by aggregating multiple decision trees. LSBoost uses gradient descent training to assign contribution coefficients to different kinds of raw materials to obtain more accurate predictions. Five key chemical elements, including carbon, silicon, manganese, phosphorus, and sulfur, which significantly influence the major performance characteristics of steel products, are selected. Steel quality prediction is conducted by predicting the contents of these chemical elements. Multiple models are constructed to predict the contents of five key chemical elements in steel products. These models are symmetrically complementary, meeting the requirements of different production scenarios and forming a more accurate and universal method for predicting the steel product’s quality. In addition, the prediction method provides a symmetric quality control system for steel product production. Experimental evaluations are conducted based on a dataset of 2012 samples from a steel plant in Liaoning Province, China. The input variables include various raw material usages, while the outputs are the content of five key chemical elements that influence the quality of steel products. The experimental results show that the models demonstrate their advantages in different performance metrics and are applicable to practical steelmaking scenarios. Full article

To address the challenges of insufficient response speed and robustness in optical attitude control systems under highly dynamic disturbances and internal uncertainties, a composite control strategy is proposed in this study. By integrating the proposed piecewise sliding control (P-SMC) with the improved active disturbance rejection control (ADRC), this strategy achieves complementary performance, which can not only suppress the disturbance but also converge to a bounded region fast. Under highly dynamic disturbances, the improved extended state observer (ESO) based on the EKF achieves rapid response with amplified state observations, and the Nonlinear State Error Feedback (NLSEF) generates a compensation signal to actively reject disturbances. Simultaneously, the robust sliding mode control (SMC) suppresses the effects of system nonlinearity and uncertainty. To address chattering and overshoot of the conventional SMC, this study proposes a novel P-SMC law which applies distinct reaching functions across different error bands. Furthermore, the key parameters of the composite scheme are globally optimized using the particle swarm optimization (PSO) algorithm to achieve Pareto-optimal trade-offs between tracking accuracy and disturbance rejection robustness. Finally, MATLAB simulation experiments validate the effectiveness of the proposed strategy under diverse representative disturbances. The results demonstrate improved performance in terms of response speed, overshoot, settling time and control input signals smoothness compared to conventional control algorithms (ADRC, C-ADRC, T-SMC-ADRC). The proposed strategy enhances the stability and robustness of optical attitude control system against internal uncertainties of system and sensor measurement noise. It achieves bounded-error steady-state tracking against random multi-source disturbances while preserving high real-time responsiveness and efficiency. Full article

Motion capture (MoCap) data derived from wearable Inertial Measurement Units is essential to applications in sports science and healthcare robotics. However, a significant amount of the potential of this data is limited due to missing data derived from sensor limitations, network issues, and environmental interference. Such limitations can introduce bias, prevent the fusion of critical data streams, and ultimately compromise the integrity of human activity analysis. Despite the plethora of data imputation techniques available, there have been few systematic performance evaluations of these techniques explicitly for the time series data of IMU-derived MoCap data. We address this by evaluating the imputation performance across three distinct contexts: univariate time series, multivariate across players, and multivariate across kinematic angles. To address this limitation, we propose a systematic comparative analysis of imputation techniques, including statistical, machine learning, and deep learning techniques, in this paper. We also introduce the first publicly available MoCap dataset specifically for the purpose of benchmarking missing value imputation, with three missingness mechanisms: missing completely at random, block missingness, and a simulated value-dependent missingness pattern simulated at the signal transition points. Using data from 53 karate practitioners performing standardized movements, we artificially generated missing values to create controlled experimental conditions. We performed experiments across the 53 subjects with 39 kinematic variables, which showed that discriminating between univariate and multivariate imputation frameworks demonstrates that multivariate imputation frameworks surpassunivariate approaches when working with more complex missingness mechanisms. Specifically, multivariate approaches achieved up to a 50% error reduction (with the MAE improving from 10.8 ± 6.9 to 5.8 ± 5.5) compared to univariate methods for transition point missingness. Specialized time series deep learning models (i.e., SAITS, BRITS, GRU-D) demonstrated a superior performance with MAE values consistently below 8.0 for univariate contexts and below 3.2 for multivariate contexts across all missing data percentages, significantly surpassing traditional machine learning and statistical methods. Notable traditional methods such as Generative Adversarial Imputation Networks and Iterative Imputers exhibited a competitive performance but remained less stable than the specialized temporal models. This work offers an important baseline for future studies, in addition to recommendations for researchers looking to increase the accuracy and robustness of MoCap data analysis, as well as integrity and trustworthiness. Full article

Atmospheric turbulence severely limits the stability and reliability of free-space optical (FSO) uplinks by inducing wavefront distortions and random intensity fluctuations. This study investigates the use of reinforcement learning (RL) with beacon-based feedback for adaptive beam shaping in a spatial light modulator (SLM)-controlled FSO link. The RL agent dynamically adjusts phase patterns to maximize received signal strength, while the beacon channel provides turbulence estimates that guide the optimization process. Experiments under low, moderate, and high turbulence levels demonstrate that incorporating beacon feedback can enhance link stability in severe conditions, reducing signal variability and suppressing extreme fluctuations. In low-turbulence scenarios, the performance is comparable to non-feedback operation, whereas under high turbulence, beacon-assisted control consistently achieves lower coefficients of variation and improved bit error rate (BER) performance. Under high turbulence replay experiments—where the best-performing RL-learned phase patterns are reapplied without learning—further show that configurations trained with feedback retain robustness, even without real-time turbulence measurements under high turbulence. These results highlight the potential of integrating contextual feedback with RL to achieve turbulence-resilient and stable optical uplinks in dynamic atmospheric environments. Full article

Early and accurate detection of distributed bearing faults is essential to prevent equipment failures and reduce downtime in industrial environments. This study explores the optimal selection of input signal sources for high-accuracy distributed fault classification, employing wavelet transform and machine learning algorithms. The primary contribution of this work is to demonstrate that robust distributed bearing fault diagnosis can be achieved through optimal sensor fusion and wavelet-based feature engineering, without the need for deep learning or high-dimensional inputs. This approach provides interpretable, computationally efficient, and generalizable fault classification, setting it apart from most existing studies that rely on larger models or more extensive data. All experiments were conducted in a controlled laboratory environment across multiple loads and speeds. A comprehensive dataset, including three-axis vibration, stray magnetic flux, and two-phase current signals, was used to diagnose six distinct bearing fault conditions. The wavelet transform is applied to extract frequency-domain features, capturing intricate fault signatures. To identify the most effective input signal combinations, we systematically evaluated Random Forest, XGBoost, and Support Vector Machine (SVM) models. The analysis reveals that specific signal pairs significantly enhance classification accuracy. Notably, combining vibration signals with stray magnetic flux consistently achieved the highest performance across models, with Random Forest reaching perfect test accuracy (100%) and SVM showing robust results. These findings underscore the importance of optimal source selection and wavelet-transformed features for improving machine learning model performance in bearing fault classification tasks. While the results are promising, validation in real-world industrial settings is needed to fully assess the method’s practical reliability and impact on predictive maintenance systems. Full article

Background: Electroencephalographic (EEG) neurofeedback training is gaining traction as a non-pharmacological treatment option for Parkinson’s disease (PD). This paper reports the first pre-registered, integrated systematic review and meta-analysis of studies examining the effects of EEG neurofeedback on cortical activity and motor function in people with PD. Method: We searched Cochrane Databases, PubMed, Embase, Scopus, Web of Science, PsycInfo, grey literature repositories, and trial registers for EEG neurofeedback studies in people with PD. We included randomized controlled trials, single-group experiments, and case studies. We assessed risk of bias using the Cochrane Risk of Bias 2 and Risk of Bias in Non-Randomized Studies tools, and we used the Grading of Recommendations, Assessment, Development and Evaluations tool to assess certainty in the evidence and resultant interpretations. Random-effects meta-analyses were performed. Results: A total of 11 studies (143 participants; Hoehn and Yahr I–IV) met the criteria for inclusion. A first meta-analysis revealed that EEG activity is modified in the prescribed way by neurofeedback interventions. The effect size is large (SMD = 1.30, 95% CI = 0.50–2.10, p = 0.001). Certainty in the estimate is high. Despite successful cortical modulation, a subsequent meta-analysis revealed inconclusive effects of EEG neurofeedback on motor symptomology. The effect size is small (SMD = 0.10, 95% CI = −1.03–1.23, p = 0.86). Certainty in the estimates is low. Narrative evidence revealed that interventions are well-received and may yield specific benefits not detected by general symptomology reports. Conclusion: EEG neurofeedback successfully modulates cortical activity in people with PD, but downstream impacts on motor function remain unclear. The neuromodulatory potential of EEG neurofeedback in people with PD is encouraging. Additional well-powered and high-quality research into the effects of EEG neurofeedback in PD is warranted. Full article

Feedthrough interference is inevitably introduced in MEMS gyroscopes due to non-ideal factors such as circuit layout design and fabrication processes, exerting non-negligible impacts on gyroscope performance. This study proposes a feedthrough suppression scheme for MEMS gyroscopes based on mixed-frequency excitation signals. Leveraging the quadratic relationship between excitation voltage and electrostatic force in capacitive resonators, the resonator is excited with a modulated signal at a non-resonant frequency while sensing vibration signals at the resonant frequency. This approach achieves linear excitation without requiring backend demodulation circuits, effectively separating desired signals from feedthrough interference in the frequency domain. A mixed-frequency excitation-based measurement and control system for MEMS gyroscopes is constructed. The influence of mismatch phenomena under non-ideal conditions on the control system is analyzed with corresponding solutions provided. Simulations and experiments validate the scheme’s effectiveness, demonstrating feedthrough suppression through both amplitude-frequency characteristics and scale factor perspectives. Test results confirm the scheme eliminates the zero introduced by feedthrough interference in the gyroscope’s amplitude-frequency response curve and reduces force-to-rebalanced detection scale factor fluctuations caused by frequency split variations by a factor of 21. Under this scheme, the gyroscope achieves zero-bias stability of 0.3118 °/h and angle random walk of 0.2443 °/h/√Hz. Full article

Grasslands cover more than 25% of the Earth’s surface and play essential ecological roles, such as forage production, supporting pollinators, and carbon sequestration. This study aimed to evaluate the recovery of a degraded pasture of Urochloa decumbens cv. Basilisk through aerial dry mass production, plant height, and foliar macronutrients concentration and uptake after fertilization with polyhalite. The experiment was carried out at the Teaching, Research, and Extension Farm of the School of Agrarian and Technological Sciences, UNESP—Dracena Campus, in a dystrophic red–yellow latosol soil. A randomized block design with four replications was used. The treatments included the following: (T1) control, (T2) N + P + liming, (T3) T2 + 30 kg ha⁻¹ K₂O (polyhalite), (T4) T2 + 60 kg ha⁻¹ K₂O (polyhalite), (T5) T2 + 60 (30 + 30) kg ha⁻¹ K₂O (polyhalite), and (T6) 60 kg ha⁻¹ K₂O (polyhalite). The treatment with N + P + liming + 60 kg ha⁻¹ K₂O (polyhalite) resulted in 93% more dry mass production when compared with the control treatment. This treatment was most effective for grassland recovery, whereas polyhalite alone was ineffective. Tissue N and S concentrations increased as a result of the addition of N + P + liming + 60 kg ha⁻¹ K₂O (polyhalite). Full article

Traditional maritime target element resolution, relying on manual experience and uniform sampling, lacks accuracy and efficiency in non-uniform sampling, missing data, and noisy scenarios. While large language models (LLMs) offer a solution, their general knowledge gaps with maritime needs limit direct application. This paper proposes a fine-tuned LLM-based adaptive optimization method for non-uniform sampling maritime target element resolution, with three key novelties: first, selecting Doubao-Seed-1.6 as the base model and conducting targeted preprocessing on maritime multi-source data to address domain adaptation gaps; second, innovating a “Prefix tuning + LoRA” hybrid strategy (encoding maritime rules via Prefix tuning, freezing 95% of base parameters via LoRA to reduce trainable parameters to <0.5%) to balance cost and performance; third, building a non-uniform sampling-model collaboration mechanism, where the fine-tuned model dynamically adjusts the sampling density via semantic understanding to solve random sampling’s “structural information imbalance”. Experiments in close, away, and avoid scenarios (vs. five control models including original LLMs, rule-only/models, and ChatGPT-4.0) show that the proposed method achieves a comprehensive final score of 0.8133—37.1% higher than the sub-optimal data-only model (0.5933) and 87.7% higher than the original general model (0.4333). In high-risk avoid scenarios, its Top-1 Accuracy (0.7333) is 46.7% higher than the sub-optimal control, and Scene-Sensitive Recall (0.7333) is 2.2 times the original model; in close and away scenarios, its Top-1 Accuracy reaches 0.8667 and 0.9000, respectively. This method enhances resolution accuracy and adaptability, promoting LLM applications in navigation. Full article

Introduction. Cervical artery dissection (CAD) is a rare condition, being one of the leading causes of stroke in patients under the age of 45, with a reported prevalence of up to 20%. The management of CAD remains controversial due to its rarity and the lack of large-scale randomized controlled trials. The aim of this study was to report the long-term outcomes of CAD in a real-world setting. Methods. This retrospective, observational, single-center study included patients diagnosed with CAD between 2010 and 2019 (approval number: 153/2015/U/Oss/AOUBo). Clinical presentation, risk factors, and medical therapies were prospectively analyzed. Management strategies included both medical and interventional approaches. Follow-up consisted of annual clinical visits and carotid duplex ultrasound (DUS), with telephone interviews every six months. The primary endpoint was defined by the overall long-term stroke/death rate and in relation to the type of medical treatment, localization of the dissection and clinical manifestations. Results. A total of 62 patients were included, predominantly male (65%) with a mean age of 58 (±2) years. Thirteen dissections (21%) were trauma-related. CAD locations included the common carotid artery in 6 cases (10%), extracranial internal carotid artery in 29 (46%), intracranial internal carotid artery in 9 (14%), and vertebral artery in 16 (25%). One patient (2%) had dissections in both the extracranial internal carotid and vertebral arteries, and another (2%) in both the vertebral and basilar arteries. Bilateral dissections were observed in 5 patients (8%). Ischemic manifestations occurred in 43 patients (68%): 10 transient ischemic attacks (16%), 17 minor strokes (27%), and 16 major strokes (25%), with ischemic lesions on cerebral CT in 31 cases (72%). Fifty-eight (93%) patients were treated medically (anticoagulants and/or antiplatelets), while 4 patients (7%) underwent surgical or endovascular intervention. The mean follow-up was 81 ± 35 months. During this period, 2 patients (4%) experienced stroke and 15 (24%) died. The estimated 10-year survival rate was 71%, and the 10-year stroke/death-free survival rate was 70%. Among medically treated patients, the 10-year stroke/death-free survival was 86% for those on anticoagulation and 67% for those on antiplatelet therapy (p = 0.1). Patients presenting with ischemic symptoms had a lower estimated 10-year stroke/death-free survival rate compared to those with non-ischemic presentations (61% vs. 69%, p = 0.7). Patients with dissection of the common carotid artery had a significantly lower estimated 10-year stroke/death-free survival rate (25%), compared to dissections in other cervical arteries (p = 0.001). Conclusions. In this real-world, single-center experience, cervical artery dissection was associated with a favorable long-term prognosis in most cases, especially among patients managed conservatively with medical therapy. Stroke and mortality rates were relatively low during extended follow-up. Although no statistically significant difference was observed between anticoagulation and antiplatelet therapy, the trend favored anticoagulation for stroke/death-free survival. Patients with CCA dissections had significantly worse 10-year stroke/death-free survival compared to those with dissections in other cervical arteries. Full article

Background: The increasing use of virtual reality (VR) has extended into medical applications, including pain management through immersive mechanisms. This study aimed to evaluate the effectiveness of the Clone Hero for reducing pain intensity, threshold and tolerance. Methods: A randomized crossover trial compared three conditions during a cold pressor test in 25 healthy volunteers over 35 years: playing Clone Hero (interactive), watching Clone Hero (control), or no intervention (placebo). Outcome measures included usability and acceptability (qualitative questionnaire), pain intensity (VAS), pain threshold, pain tolerance, physical activity (IPAQ), and adverse effects. Results: Twenty-five participants completed the study. Overall satisfaction was high, with 92% reporting a positive experience. The Clone Hero group showed significantly lower pain intensity scores (4.9 ± 0.49) than the placebo (5.6 ± 0.48; p = 0.037) and control groups (6.1 ± 0.42; p = 0.004). Pain threshold was higher in the Clone Hero group (74.45 ± 20.7 s) compared to the placebo (62.91 ± 18.58; p < 0.001) and control (43 ± 14.77; p = 0.001). Pain tolerance was also greater (127.6 ± 9.46 s) versus the placebo (p = 0.021) and control (p = 0.001). No serious adverse effects were reported. Conclusions: Interactive pain management interventions demonstrated high levels of acceptability and user satisfaction, and may enhance pain modulation more effectively than passive or control. Full article

Background/Objectives: Children and adolescents with neurodevelopmental disorders (NDDs) often experience deficits in executive functioning and emotional regulation, which impact their academic, social, and behavioral development. While physical activity is increasingly recognized as a promising non-pharmacological intervention, the specific effects on cognitive and emotional domains remain heterogeneous. This systematic review and meta-analysis aimed to assess the efficacy of physical–motor interventions in improving executive functions and emotional regulation in youths with NDDs. Methods: Following PRISMA 2020 guidelines, a comprehensive search of five databases was conducted (2010–2024) to identify randomized controlled trials (RCTs) evaluating the effects of structured physical activity programs on executive and emotional outcomes in children and adolescents diagnosed with NDDs. A total of 22 RCTs were included in the qualitative synthesis, while 16 were included in the quantitative analysis. Effect sizes were calculated using a random effects model, while heterogeneity was assessed with the Q, I², Tau², and Egger’s tests. Results: Physical activity interventions demonstrated a non-significant effect on executive functioning (g = 0.492; p = 0.215; 95% CI: −0.286 to 1.269). Although the point estimate suggested a small-to-moderate effect, the wide confidence interval and lack of statistical significance prevent firm conclusions. In contrast, a large and significant effect was observed on emotional regulation outcomes (g = −1.204; p < 0.001; 95% CI: −1.688 to −0.655), despite moderate heterogeneity (I² = 72.3%). Several studies also reported specific improvements in working memory, cognitive flexibility, and emotional control. Conclusions: Structured physical activity may be an effective complementary intervention for improving emotional regulation in youth with NDDs, with less consistent evidence for executive functioning. Future research should clarify optimal protocols and target populations to enhance intervention effectiveness. Full article

Remote Access Trojans (RATs) pose a serious cybersecurity risk due to their stealthy control over compromised systems. This study presents a detection framework that integrates host, network, and newly engineered behavioral features to enhance the identification of RATs. Two sets of experiments were performed: (i) using the original dataset only, and (ii) using an extended dataset with ten engineered features and importance analysis. The framework was evaluated on a public Kaggle dataset of an RAT and benign traffic. Eight machine learning classifiers were tested, including three baseline methods, four ensemble approaches, and a neural network. Results show that the engineered hybrid feature set substantially improves detection performance. Among the tested algorithms, Random Forest and MLP achieved the strongest performance, with accuracies of 98% and 97%, respectively, while Gradient Boosting and LightGBM also performed competitively. Performance was assessed using multiple metrics, and to gain deeper insight into model learning behavior, learning curves and Precision–Recall curves were analyzed. The results demonstrate how well hybrid feature modeling, neural networks, and ensemble machine learning techniques may improve RAT identification. In future work, exploring the use of explainable ML methods may improve the detection capabilities. Full article